Overview

Brought to you by YData

Dataset statistics

Number of variables31
Number of observations2000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory484.5 KiB
Average record size in memory248.1 B

Variable types

Numeric18
Categorical4
Boolean9

Alerts

Age is highly overall correlated with Age Youngest Child and 1 other fieldsHigh correlation
Age Youngest Child is highly overall correlated with Age and 1 other fieldsHigh correlation
Annual value is highly overall correlated with Number of Loan Accounts and 1 other fieldsHigh correlation
Average Balance Feed Index is highly overall correlated with Number of Products and 2 other fieldsHigh correlation
Household Debt to Equity Ratio is highly overall correlated with Percentage White Collar WorkersHigh correlation
Months Current Account is highly overall correlated with Months as a CustomerHigh correlation
Months as a Customer is highly overall correlated with Months Current AccountHigh correlation
Number of Loan Accounts is highly overall correlated with Annual valueHigh correlation
Number of Products is highly overall correlated with Annual value and 3 other fieldsHigh correlation
Number of Transactions is highly overall correlated with Average Balance Feed Index and 2 other fieldsHigh correlation
Percentage White Collar Workers is highly overall correlated with Household Debt to Equity RatioHigh correlation
Personal Debt to Equity Ratio is highly overall correlated with Age and 1 other fieldsHigh correlation
RFM Score is highly overall correlated with Average Balance Feed Index and 2 other fieldsHigh correlation
Has Bad Payment Record is highly imbalanced (95.8%) Imbalance
Interested in Personal Loan is highly imbalanced (76.9%) Imbalance
Interested in Credit Card is highly imbalanced (50.2%) Imbalance
Customer ID has unique values Unique
Number of Products has 1217 (60.9%) zeros Zeros
RFM Score has 1307 (65.3%) zeros Zeros
Average Balance Feed Index has 1305 (65.2%) zeros Zeros
Number of Transactions has 1305 (65.2%) zeros Zeros
Months Current Account has 28 (1.4%) zeros Zeros
Number of Loan Accounts has 850 (42.5%) zeros Zeros
Number of Call Center Contacts has 288 (14.4%) zeros Zeros
Age Youngest Child has 454 (22.7%) zeros Zeros
Annual value has 525 (26.2%) zeros Zeros

Reproduction

Analysis started2024-11-20 19:41:34.225502
Analysis finished2024-11-20 19:42:26.236722
Duration52.01 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Age
Real number (ℝ)

High correlation 

Distinct73
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.4955
Minimum10
Maximum83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-11-20T20:42:26.431637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile20
Q131
median39
Q346
95-th percentile61
Maximum83
Range73
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.076005
Coefficient of variation (CV)0.30575648
Kurtosis0.48307872
Mean39.4955
Median Absolute Deviation (MAD)8
Skewness0.45225504
Sum78991
Variance145.82989
MonotonicityNot monotonic
2024-11-20T20:42:26.758581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 87
 
4.3%
39 85
 
4.2%
36 83
 
4.2%
38 76
 
3.8%
41 72
 
3.6%
37 70
 
3.5%
43 68
 
3.4%
34 66
 
3.3%
42 66
 
3.3%
44 63
 
3.1%
Other values (63) 1264
63.2%
ValueCountFrequency (%)
10 2
 
0.1%
11 4
 
0.2%
12 1
 
0.1%
13 3
 
0.1%
14 7
 
0.4%
15 11
0.5%
16 8
 
0.4%
17 7
 
0.4%
18 23
1.1%
19 18
0.9%
ValueCountFrequency (%)
83 3
 
0.1%
82 2
 
0.1%
81 1
 
0.1%
80 1
 
0.1%
79 2
 
0.1%
78 1
 
0.1%
76 1
 
0.1%
75 4
0.2%
74 1
 
0.1%
73 8
0.4%

Months as a Customer
Categorical

High correlation 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
36.0
573 
24.0
539 
12.0
328 
0.0
313 
48.0
247 

Length

Max length4
Median length4
Mean length3.8435
Min length3

Characters and Unicode

Total characters7687
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row24.0
2nd row12.0
3rd row36.0
4th row0.0
5th row48.0

Common Values

ValueCountFrequency (%)
36.0 573
28.6%
24.0 539
27.0%
12.0 328
16.4%
0.0 313
15.7%
48.0 247
12.3%

Length

2024-11-20T20:42:27.107692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-20T20:42:27.372346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
36.0 573
28.6%
24.0 539
27.0%
12.0 328
16.4%
0.0 313
15.7%
48.0 247
12.3%

Most occurring characters

ValueCountFrequency (%)
0 2313
30.1%
. 2000
26.0%
2 867
 
11.3%
4 786
 
10.2%
3 573
 
7.5%
6 573
 
7.5%
1 328
 
4.3%
8 247
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7687
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2313
30.1%
. 2000
26.0%
2 867
 
11.3%
4 786
 
10.2%
3 573
 
7.5%
6 573
 
7.5%
1 328
 
4.3%
8 247
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7687
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2313
30.1%
. 2000
26.0%
2 867
 
11.3%
4 786
 
10.2%
3 573
 
7.5%
6 573
 
7.5%
1 328
 
4.3%
8 247
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7687
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2313
30.1%
. 2000
26.0%
2 867
 
11.3%
4 786
 
10.2%
3 573
 
7.5%
6 573
 
7.5%
1 328
 
4.3%
8 247
 
3.2%

Number of Products
Real number (ℝ)

High correlation  Zeros 

Distinct47
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.343
Minimum0
Maximum56
Zeros1217
Zeros (%)60.9%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-11-20T20:42:27.602716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile9
Maximum56
Range56
Interquartile range (IQR)2

Descriptive statistics

Standard deviation6.3680159
Coefficient of variation (CV)2.7178899
Kurtosis25.359853
Mean2.343
Median Absolute Deviation (MAD)0
Skewness4.7713752
Sum4686
Variance40.551627
MonotonicityNot monotonic
2024-11-20T20:42:27.833725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 1217
60.9%
2 199
 
10.0%
3 157
 
7.8%
4 123
 
6.2%
1 112
 
5.6%
5 51
 
2.5%
6 22
 
1.1%
7 10
 
0.5%
25 8
 
0.4%
9 8
 
0.4%
Other values (37) 93
 
4.7%
ValueCountFrequency (%)
0 1217
60.9%
1 112
 
5.6%
2 199
 
10.0%
3 157
 
7.8%
4 123
 
6.2%
5 51
 
2.5%
6 22
 
1.1%
7 10
 
0.5%
8 4
 
0.2%
9 8
 
0.4%
ValueCountFrequency (%)
56 1
 
0.1%
55 1
 
0.1%
52 1
 
0.1%
51 1
 
0.1%
47 2
0.1%
46 1
 
0.1%
43 4
0.2%
42 2
0.1%
41 2
0.1%
39 2
0.1%

RFM Score
Real number (ℝ)

High correlation  Zeros 

Distinct651
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.069605
Minimum0
Maximum35.762
Zeros1307
Zeros (%)65.3%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-11-20T20:42:28.067710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38.929
95-th percentile13.33605
Maximum35.762
Range35.762
Interquartile range (IQR)8.929

Descriptive statistics

Standard deviation6.3911805
Coefficient of variation (CV)1.570467
Kurtosis3.8288281
Mean4.069605
Median Absolute Deviation (MAD)0
Skewness1.7817038
Sum8139.21
Variance40.847189
MonotonicityNot monotonic
2024-11-20T20:42:28.307588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1307
65.3%
7.582 3
 
0.1%
9.92 3
 
0.1%
8.262 3
 
0.1%
8.412 2
 
0.1%
8.446 2
 
0.1%
8.969 2
 
0.1%
8.316 2
 
0.1%
7.816 2
 
0.1%
8.657 2
 
0.1%
Other values (641) 672
33.6%
ValueCountFrequency (%)
0 1307
65.3%
7.279 1
 
0.1%
7.292 1
 
0.1%
7.316 1
 
0.1%
7.319 1
 
0.1%
7.337 1
 
0.1%
7.35 1
 
0.1%
7.382 1
 
0.1%
7.406 1
 
0.1%
7.416 1
 
0.1%
ValueCountFrequency (%)
35.762 1
0.1%
34.688 1
0.1%
33.879 1
0.1%
33.707 1
0.1%
33.289 1
0.1%
33.109 1
0.1%
33.097 1
0.1%
32.937 1
0.1%
32.731 1
0.1%
32.338 1
0.1%

Average Balance Feed Index
Real number (ℝ)

High correlation  Zeros 

Distinct279
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.6675
Minimum0
Maximum1208
Zeros1305
Zeros (%)65.2%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-11-20T20:42:28.546772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3119
95-th percentile429
Maximum1208
Range1208
Interquartile range (IQR)119

Descriptive statistics

Standard deviation153.08673
Coefficient of variation (CV)1.8977497
Kurtosis7.9463113
Mean80.6675
Median Absolute Deviation (MAD)0
Skewness2.5740984
Sum161335
Variance23435.546
MonotonicityNot monotonic
2024-11-20T20:42:28.935988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1305
65.2%
299 25
 
1.2%
129 23
 
1.1%
89 22
 
1.1%
69 19
 
0.9%
249 16
 
0.8%
179 15
 
0.8%
59 15
 
0.8%
199 15
 
0.8%
109 14
 
0.7%
Other values (269) 531
26.6%
ValueCountFrequency (%)
0 1305
65.2%
4 1
 
0.1%
8 1
 
0.1%
9 1
 
0.1%
10 2
 
0.1%
13 2
 
0.1%
15 1
 
0.1%
16 1
 
0.1%
17 1
 
0.1%
19 1
 
0.1%
ValueCountFrequency (%)
1208 1
0.1%
970 1
0.1%
963 1
0.1%
958 1
0.1%
948 1
0.1%
932 1
0.1%
909 1
0.1%
905 1
0.1%
847 1
0.1%
807 1
0.1%

Number of Transactions
Real number (ℝ)

High correlation  Zeros 

Distinct29
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2485
Minimum0
Maximum29
Zeros1305
Zeros (%)65.2%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-11-20T20:42:29.143211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum29
Range29
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.6377692
Coefficient of variation (CV)2.9137118
Kurtosis26.069999
Mean1.2485
Median Absolute Deviation (MAD)0
Skewness4.9227948
Sum2497
Variance13.233364
MonotonicityNot monotonic
2024-11-20T20:42:29.342212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 1305
65.2%
1 307
 
15.3%
2 182
 
9.1%
3 96
 
4.8%
4 11
 
0.5%
5 10
 
0.5%
15 7
 
0.4%
18 6
 
0.3%
11 6
 
0.3%
25 6
 
0.3%
Other values (19) 64
 
3.2%
ValueCountFrequency (%)
0 1305
65.2%
1 307
 
15.3%
2 182
 
9.1%
3 96
 
4.8%
4 11
 
0.5%
5 10
 
0.5%
6 4
 
0.2%
7 4
 
0.2%
8 4
 
0.2%
9 5
 
0.2%
ValueCountFrequency (%)
29 1
 
0.1%
27 3
0.1%
26 4
0.2%
25 6
0.3%
24 4
0.2%
23 5
0.2%
22 3
0.1%
21 2
 
0.1%
20 2
 
0.1%
19 2
 
0.1%

Personal Debt to Equity Ratio
Real number (ℝ)

High correlation 

Distinct73
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.4955
Minimum10
Maximum83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-11-20T20:42:29.594251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile20
Q131
median39
Q346
95-th percentile61
Maximum83
Range73
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.076005
Coefficient of variation (CV)0.30575648
Kurtosis0.48307872
Mean39.4955
Median Absolute Deviation (MAD)8
Skewness0.45225504
Sum78991
Variance145.82989
MonotonicityNot monotonic
2024-11-20T20:42:29.840580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 87
 
4.3%
39 85
 
4.2%
36 83
 
4.2%
38 76
 
3.8%
41 72
 
3.6%
37 70
 
3.5%
43 68
 
3.4%
34 66
 
3.3%
42 66
 
3.3%
44 63
 
3.1%
Other values (63) 1264
63.2%
ValueCountFrequency (%)
10 2
 
0.1%
11 4
 
0.2%
12 1
 
0.1%
13 3
 
0.1%
14 7
 
0.4%
15 11
0.5%
16 8
 
0.4%
17 7
 
0.4%
18 23
1.1%
19 18
0.9%
ValueCountFrequency (%)
83 3
 
0.1%
82 2
 
0.1%
81 1
 
0.1%
80 1
 
0.1%
79 2
 
0.1%
78 1
 
0.1%
76 1
 
0.1%
75 4
0.2%
74 1
 
0.1%
73 8
0.4%

Months Current Account
Real number (ℝ)

High correlation  Zeros 

Distinct45
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.9105
Minimum-7
Maximum41
Zeros28
Zeros (%)1.4%
Negative317
Negative (%)15.8%
Memory size15.8 KiB
2024-11-20T20:42:30.068636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-7
5-th percentile-5
Q18
median18
Q329
95-th percentile38
Maximum41
Range48
Interquartile range (IQR)21

Descriptive statistics

Standard deviation13.583068
Coefficient of variation (CV)0.75838576
Kurtosis-1.0643807
Mean17.9105
Median Absolute Deviation (MAD)11
Skewness-0.18494332
Sum35821
Variance184.49974
MonotonicityNot monotonic
2024-11-20T20:42:30.280038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
23 72
 
3.6%
-4 68
 
3.4%
-6 61
 
3.0%
17 61
 
3.0%
10 60
 
3.0%
14 54
 
2.7%
27 54
 
2.7%
37 54
 
2.7%
5 54
 
2.7%
28 54
 
2.7%
Other values (35) 1408
70.4%
ValueCountFrequency (%)
-7 5
 
0.2%
-6 61
3.0%
-5 51
2.5%
-4 68
3.4%
-3 45
2.2%
-2 44
2.2%
-1 43
2.1%
0 28
1.4%
5 54
2.7%
6 43
2.1%
ValueCountFrequency (%)
41 11
 
0.5%
40 28
1.4%
39 36
1.8%
38 44
2.2%
37 54
2.7%
36 50
2.5%
35 48
2.4%
34 45
2.2%
33 53
2.6%
32 36
1.8%

Number of Loan Accounts
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1145
Minimum0
Maximum7
Zeros850
Zeros (%)42.5%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-11-20T20:42:30.450359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3886518
Coefficient of variation (CV)1.2459864
Kurtosis3.2395037
Mean1.1145
Median Absolute Deviation (MAD)1
Skewness1.6897097
Sum2229
Variance1.9283539
MonotonicityNot monotonic
2024-11-20T20:42:30.637340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 850
42.5%
1 613
30.6%
2 244
 
12.2%
3 156
 
7.8%
4 77
 
3.9%
5 29
 
1.5%
7 21
 
1.1%
6 10
 
0.5%
ValueCountFrequency (%)
0 850
42.5%
1 613
30.6%
2 244
 
12.2%
3 156
 
7.8%
4 77
 
3.9%
5 29
 
1.5%
6 10
 
0.5%
7 21
 
1.1%
ValueCountFrequency (%)
7 21
 
1.1%
6 10
 
0.5%
5 29
 
1.5%
4 77
 
3.9%
3 156
 
7.8%
2 244
 
12.2%
1 613
30.6%
0 850
42.5%

Customer ID
Real number (ℝ)

Unique 

Distinct2000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45405.118
Minimum8
Maximum97651
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-11-20T20:42:30.861326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile4370.8
Q124628.75
median45300
Q367063.25
95-th percentile85851.35
Maximum97651
Range97643
Interquartile range (IQR)42434.5

Descriptive statistics

Standard deviation25900.767
Coefficient of variation (CV)0.57043718
Kurtosis-1.0937546
Mean45405.118
Median Absolute Deviation (MAD)21178.5
Skewness-0.011922302
Sum90810235
Variance6.7084975 × 108
MonotonicityNot monotonic
2024-11-20T20:42:31.114147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76719 1
 
0.1%
5409 1
 
0.1%
5477 1
 
0.1%
5507 1
 
0.1%
8 1
 
0.1%
158 1
 
0.1%
173 1
 
0.1%
5408 1
 
0.1%
5579 1
 
0.1%
5695 1
 
0.1%
Other values (1990) 1990
99.5%
ValueCountFrequency (%)
8 1
0.1%
37 1
0.1%
53 1
0.1%
93 1
0.1%
104 1
0.1%
158 1
0.1%
173 1
0.1%
202 1
0.1%
311 1
0.1%
323 1
0.1%
ValueCountFrequency (%)
97651 1
0.1%
97487 1
0.1%
97412 1
0.1%
97262 1
0.1%
96719 1
0.1%
96599 1
0.1%
96274 1
0.1%
96176 1
0.1%
96133 1
0.1%
96056 1
0.1%

Has Bad Payment Record
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
0
1991 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1991
99.6%
1 9
 
0.4%

Length

2024-11-20T20:42:31.309127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-20T20:42:31.456122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1991
99.6%
1 9
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 1991
99.6%
1 9
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1991
99.6%
1 9
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1991
99.6%
1 9
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1991
99.6%
1 9
 
0.4%

Members Within Household
Real number (ℝ)

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7055
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-11-20T20:42:31.602843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7794249
Coefficient of variation (CV)0.48021182
Kurtosis-1.1234931
Mean3.7055
Median Absolute Deviation (MAD)2
Skewness0.05245727
Sum7411
Variance3.1663529
MonotonicityNot monotonic
2024-11-20T20:42:31.790703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 341
17.1%
4 331
16.6%
2 329
16.4%
6 325
16.2%
5 315
15.8%
1 272
13.6%
7 87
 
4.3%
ValueCountFrequency (%)
1 272
13.6%
2 329
16.4%
3 341
17.1%
4 331
16.6%
5 315
15.8%
6 325
16.2%
7 87
 
4.3%
ValueCountFrequency (%)
7 87
 
4.3%
6 325
16.2%
5 315
15.8%
4 331
16.6%
3 341
17.1%
2 329
16.4%
1 272
13.6%

Number of Call Center Contacts
Real number (ℝ)

Zeros 

Distinct12
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.951
Minimum0
Maximum11
Zeros288
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-11-20T20:42:32.005609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median9
Q310
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.5888771
Coefficient of variation (CV)0.45137431
Kurtosis0.73096418
Mean7.951
Median Absolute Deviation (MAD)1
Skewness-1.4645572
Sum15902
Variance12.880039
MonotonicityNot monotonic
2024-11-20T20:42:32.185652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 497
24.9%
11 402
20.1%
9 390
19.5%
0 288
14.4%
8 237
11.8%
7 97
 
4.9%
6 49
 
2.5%
5 19
 
0.9%
1 15
 
0.8%
4 4
 
0.2%
Other values (2) 2
 
0.1%
ValueCountFrequency (%)
0 288
14.4%
1 15
 
0.8%
2 1
 
0.1%
3 1
 
0.1%
4 4
 
0.2%
5 19
 
0.9%
6 49
 
2.5%
7 97
 
4.9%
8 237
11.8%
9 390
19.5%
ValueCountFrequency (%)
11 402
20.1%
10 497
24.9%
9 390
19.5%
8 237
11.8%
7 97
 
4.9%
6 49
 
2.5%
5 19
 
0.9%
4 4
 
0.2%
3 1
 
0.1%
2 1
 
0.1%

Gender
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
M
1017 
F
983 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowM
3rd rowF
4th rowM
5th rowF

Common Values

ValueCountFrequency (%)
M 1017
50.8%
F 983
49.1%

Length

2024-11-20T20:42:32.382601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-20T20:42:32.552914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
m 1017
50.8%
f 983
49.1%

Most occurring characters

ValueCountFrequency (%)
M 1017
50.8%
F 983
49.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 1017
50.8%
F 983
49.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 1017
50.8%
F 983
49.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 1017
50.8%
F 983
49.1%

Marital Status
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
S
973 
M
526 
U
501 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowM
3rd rowU
4th rowU
5th rowS

Common Values

ValueCountFrequency (%)
S 973
48.6%
M 526
26.3%
U 501
25.1%

Length

2024-11-20T20:42:32.727486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-20T20:42:32.906855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
s 973
48.6%
m 526
26.3%
u 501
25.1%

Most occurring characters

ValueCountFrequency (%)
S 973
48.6%
M 526
26.3%
U 501
25.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 973
48.6%
M 526
26.3%
U 501
25.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 973
48.6%
M 526
26.3%
U 501
25.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 973
48.6%
M 526
26.3%
U 501
25.1%

Age Youngest Child
Real number (ℝ)

High correlation  Zeros 

Distinct56
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.2915
Minimum0
Maximum57
Zeros454
Zeros (%)22.7%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-11-20T20:42:33.282561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median10
Q317
95-th percentile32
Maximum57
Range57
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.614559
Coefficient of variation (CV)0.94004862
Kurtosis1.0801287
Mean11.2915
Median Absolute Deviation (MAD)8
Skewness1.0709032
Sum22583
Variance112.66886
MonotonicityNot monotonic
2024-11-20T20:42:33.567922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 454
22.7%
12 80
 
4.0%
7 78
 
3.9%
5 77
 
3.9%
14 75
 
3.8%
11 75
 
3.8%
13 74
 
3.7%
10 73
 
3.6%
8 61
 
3.0%
4 60
 
3.0%
Other values (46) 893
44.6%
ValueCountFrequency (%)
0 454
22.7%
1 35
 
1.8%
2 57
 
2.9%
3 46
 
2.3%
4 60
 
3.0%
5 77
 
3.9%
6 59
 
2.9%
7 78
 
3.9%
8 61
 
3.0%
9 58
 
2.9%
ValueCountFrequency (%)
57 2
0.1%
55 1
 
0.1%
54 1
 
0.1%
53 1
 
0.1%
52 1
 
0.1%
51 2
0.1%
49 1
 
0.1%
48 2
0.1%
47 2
0.1%
46 3
0.1%
Distinct21
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.9395
Minimum2
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-11-20T20:42:33.787518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q18
median11
Q314
95-th percentile17
Maximum22
Range20
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6130827
Coefficient of variation (CV)0.3302786
Kurtosis-0.23196068
Mean10.9395
Median Absolute Deviation (MAD)3
Skewness0.30597017
Sum21879
Variance13.054367
MonotonicityNot monotonic
2024-11-20T20:42:34.021915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
9 257
12.8%
10 223
11.2%
7 205
10.2%
11 199
10.0%
14 183
9.2%
12 154
7.7%
8 136
6.8%
13 134
6.7%
15 121
 
6.0%
16 83
 
4.2%
Other values (11) 305
15.2%
ValueCountFrequency (%)
2 7
 
0.4%
3 6
 
0.3%
4 50
 
2.5%
5 28
 
1.4%
6 79
 
4.0%
7 205
10.2%
8 136
6.8%
9 257
12.8%
10 223
11.2%
11 199
10.0%
ValueCountFrequency (%)
22 4
 
0.2%
21 8
 
0.4%
20 16
 
0.8%
19 20
 
1.0%
18 48
 
2.4%
17 39
 
1.9%
16 83
4.2%
15 121
6.0%
14 183
9.2%
13 134
6.7%

Percentage White Collar Workers
Real number (ℝ)

High correlation 

Distinct54
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.5825
Minimum9
Maximum66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-11-20T20:42:34.337737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile18
Q127
median32
Q338
95-th percentile47
Maximum66
Range57
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.9512712
Coefficient of variation (CV)0.27472635
Kurtosis0.80542887
Mean32.5825
Median Absolute Deviation (MAD)6
Skewness0.44919379
Sum65165
Variance80.125256
MonotonicityNot monotonic
2024-11-20T20:42:34.577862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 129
 
6.5%
27 110
 
5.5%
31 108
 
5.4%
36 105
 
5.2%
32 99
 
5.0%
29 91
 
4.5%
39 91
 
4.5%
28 73
 
3.6%
35 72
 
3.6%
25 71
 
3.5%
Other values (44) 1051
52.5%
ValueCountFrequency (%)
9 1
 
0.1%
10 5
 
0.2%
11 4
 
0.2%
12 4
 
0.2%
13 3
 
0.1%
14 9
 
0.4%
15 4
 
0.2%
16 8
 
0.4%
17 19
 
0.9%
18 58
2.9%
ValueCountFrequency (%)
66 1
 
0.1%
65 12
0.6%
64 4
 
0.2%
61 3
 
0.1%
58 6
0.3%
57 1
 
0.1%
56 3
 
0.1%
55 6
0.3%
54 1
 
0.1%
53 3
 
0.1%

Household Debt to Equity Ratio
Real number (ℝ)

High correlation 

Distinct59
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.9055
Minimum19
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-11-20T20:42:34.816379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile38
Q149
median56
Q362
95-th percentile69
Maximum80
Range61
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.6610682
Coefficient of variation (CV)0.17595811
Kurtosis0.51863476
Mean54.9055
Median Absolute Deviation (MAD)7
Skewness-0.48832405
Sum109811
Variance93.336238
MonotonicityNot monotonic
2024-11-20T20:42:35.089744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 153
 
7.6%
64 132
 
6.6%
58 106
 
5.3%
56 84
 
4.2%
59 83
 
4.2%
57 81
 
4.0%
61 80
 
4.0%
45 77
 
3.9%
53 74
 
3.7%
50 66
 
3.3%
Other values (49) 1064
53.2%
ValueCountFrequency (%)
19 3
 
0.1%
21 1
 
0.1%
22 3
 
0.1%
23 3
 
0.1%
24 2
 
0.1%
25 9
0.4%
26 1
 
0.1%
27 5
0.2%
28 1
 
0.1%
30 2
 
0.1%
ValueCountFrequency (%)
80 1
 
0.1%
79 2
 
0.1%
78 5
 
0.2%
77 7
0.4%
76 2
 
0.1%
75 8
0.4%
74 3
 
0.1%
73 12
0.6%
72 5
 
0.2%
71 16
0.8%

Income
Real number (ℝ)

Distinct714
Distinct (%)35.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50069.633
Minimum17418
Maximum80874
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-11-20T20:42:35.401931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum17418
5-th percentile31855
Q142562.75
median49742.5
Q357415
95-th percentile68819
Maximum80874
Range63456
Interquartile range (IQR)14852.25

Descriptive statistics

Standard deviation11510.929
Coefficient of variation (CV)0.22989842
Kurtosis-0.22587385
Mean50069.633
Median Absolute Deviation (MAD)7574.5
Skewness0.095608936
Sum1.0013927 × 108
Variance1.325015 × 108
MonotonicityNot monotonic
2024-11-20T20:42:35.707482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42752 45
 
2.2%
65263 41
 
2.1%
37502 40
 
2.0%
40287 32
 
1.6%
49921 20
 
1.0%
43935 19
 
0.9%
52174 19
 
0.9%
63861 18
 
0.9%
46755 14
 
0.7%
56482 14
 
0.7%
Other values (704) 1738
86.9%
ValueCountFrequency (%)
17418 2
 
0.1%
20666 1
 
0.1%
21103 9
0.4%
21420 2
 
0.1%
21766 2
 
0.1%
21974 1
 
0.1%
22415 2
 
0.1%
22729 1
 
0.1%
23751 4
0.2%
24450 4
0.2%
ValueCountFrequency (%)
80874 1
 
0.1%
80647 7
0.4%
80392 2
 
0.1%
79747 5
0.2%
79287 5
0.2%
78162 5
0.2%
77894 1
 
0.1%
76547 2
 
0.1%
75618 1
 
0.1%
75546 1
 
0.1%

Weeks Since Last Offer
Real number (ℝ)

Distinct52
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.8415
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-11-20T20:42:35.967069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median25
Q339
95-th percentile50
Maximum52
Range51
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.017395
Coefficient of variation (CV)0.58113478
Kurtosis-1.2001272
Mean25.8415
Median Absolute Deviation (MAD)13
Skewness0.041411306
Sum51683
Variance225.52214
MonotonicityNot monotonic
2024-11-20T20:42:36.232494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 49
 
2.5%
7 48
 
2.4%
13 46
 
2.3%
9 46
 
2.3%
23 46
 
2.3%
20 46
 
2.3%
18 45
 
2.2%
1 45
 
2.2%
2 44
 
2.2%
33 44
 
2.2%
Other values (42) 1541
77.0%
ValueCountFrequency (%)
1 45
2.2%
2 44
2.2%
3 36
1.8%
4 41
2.1%
5 41
2.1%
6 38
1.9%
7 48
2.4%
8 41
2.1%
9 46
2.3%
10 39
1.9%
ValueCountFrequency (%)
52 30
1.5%
51 43
2.1%
50 28
1.4%
49 34
1.7%
48 40
2.0%
47 43
2.1%
46 34
1.7%
45 29
1.5%
44 44
2.2%
43 40
2.0%

Homeowner
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
False
1443 
True
557 
ValueCountFrequency (%)
False 1443
72.2%
True 557
 
27.9%
2024-11-20T20:42:36.487341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
False
1742 
True
258 
ValueCountFrequency (%)
False 1742
87.1%
True 258
 
12.9%
2024-11-20T20:42:36.712410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
False
1753 
True
247 
ValueCountFrequency (%)
False 1753
87.6%
True 247
 
12.3%
2024-11-20T20:42:36.923564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
False
1778 
True
222 
ValueCountFrequency (%)
False 1778
88.9%
True 222
 
11.1%
2024-11-20T20:42:37.141351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
False
1772 
True
228 
ValueCountFrequency (%)
False 1772
88.6%
True 228
 
11.4%
2024-11-20T20:42:37.382368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Annual value
Real number (ℝ)

High correlation  Zeros 

Distinct107
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181.74
Minimum0
Maximum1680
Zeros525
Zeros (%)26.2%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2024-11-20T20:42:37.625545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median100
Q3230
95-th percentile670.5
Maximum1680
Range1680
Interquartile range (IQR)230

Descriptive statistics

Standard deviation236.09181
Coefficient of variation (CV)1.2990635
Kurtosis9.7506733
Mean181.74
Median Absolute Deviation (MAD)100
Skewness2.7220251
Sum363480
Variance55739.342
MonotonicityNot monotonic
2024-11-20T20:42:37.964001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 525
26.2%
100 396
19.8%
200 135
 
6.8%
60 90
 
4.5%
300 86
 
4.3%
120 61
 
3.0%
90 56
 
2.8%
160 51
 
2.5%
190 47
 
2.4%
400 44
 
2.2%
Other values (97) 509
25.4%
ValueCountFrequency (%)
0 525
26.2%
30 42
 
2.1%
60 90
 
4.5%
90 56
 
2.8%
100 396
19.8%
120 61
 
3.0%
130 33
 
1.7%
150 19
 
0.9%
160 51
 
2.5%
180 5
 
0.2%
ValueCountFrequency (%)
1680 2
0.1%
1660 1
0.1%
1650 1
0.1%
1630 1
0.1%
1510 1
0.1%
1470 1
0.1%
1440 1
0.1%
1410 2
0.1%
1390 1
0.1%
1360 1
0.1%

Interested in Personal Loan
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
False
1925 
True
 
75
ValueCountFrequency (%)
False 1925
96.2%
True 75
 
3.8%
2024-11-20T20:42:38.474275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
False
1761 
True
239 
ValueCountFrequency (%)
False 1761
88.0%
True 239
 
11.9%
2024-11-20T20:42:38.722273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
False
1776 
True
224 
ValueCountFrequency (%)
False 1776
88.8%
True 224
 
11.2%
2024-11-20T20:42:38.920761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Interested in Credit Card
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
False
1781 
True
219 
ValueCountFrequency (%)
False 1781
89.0%
True 219
 
10.9%
2024-11-20T20:42:39.099082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Interactions

2024-11-20T20:42:21.628237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:36.182937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:38.064336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:39.765142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:41.691847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:44.143264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:47.200500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:50.091371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:52.591043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:56.195803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:59.166500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:01.915304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:05.019262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:07.933330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:10.444978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:12.709412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:15.806400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:18.609068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:21.786673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:36.303781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:38.162708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:39.861552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:41.803838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:44.313076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:47.351110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:50.226843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:52.740474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:56.412445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:59.318120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:02.067044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:05.187110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:08.102637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:10.556177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:12.836916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:15.972028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:18.802476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:21.943679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:36.406774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:38.248881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:39.948366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:41.919960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:44.457949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:47.508876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:50.366818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:52.886698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:56.613874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:59.457775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:02.201745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:05.328303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:08.245570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:10.665602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:12.962215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:16.161466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:18.955298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:22.100179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:36.516901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:38.349259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:40.045914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:42.031960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:44.609197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:47.660390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:50.511847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:53.073598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:56.824800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:59.726483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:02.374334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:05.514646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:08.409944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:10.784581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:13.111939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:16.353719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:19.271358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:22.268159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:36.614509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:38.447577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:40.144290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:42.157138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:44.758894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:47.806814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:50.650097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:53.234862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:57.023957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:59.872197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:02.522890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:05.738336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:08.562093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:10.904737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:13.272820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:16.500460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:19.406275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:22.423444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:36.713657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:38.547614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:40.242421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:42.277768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:44.922407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:47.947073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:50.794559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:53.427870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:57.211339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:00.020597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:02.674124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:05.907462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:08.714960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:11.016618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:13.432555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:16.663496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:19.545835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:22.595868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:36.814339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:38.646211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:40.343474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:42.401125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:45.092854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:48.104662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:50.955251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:53.767057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:57.427072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:00.168354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:02.833547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:06.065069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:08.865388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:11.133010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-11-20T20:42:19.694238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:22.752774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:36.912595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:38.747878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:40.431565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:42.510344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:45.248218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-11-20T20:41:57.592705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-11-20T20:42:09.133026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:11.378316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-11-20T20:42:19.981684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-11-20T20:41:45.554045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-11-20T20:41:51.359302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-11-20T20:42:14.316984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:17.313051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:20.123953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:23.332319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:37.204112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:39.031543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:40.845328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:42.860587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:45.786043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:48.872014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:51.489708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:54.508090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:58.038791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:00.772709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:03.520420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:06.667620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:09.387345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:11.604038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:14.486879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:17.468949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:20.275515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:23.516476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:37.298651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:39.119662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:40.955941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:42.992448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:45.960140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:49.024861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:51.607923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:54.714654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:58.174685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:00.921323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:03.672471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:06.830473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:09.507612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:11.728720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:14.681922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:17.613272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:20.436373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:23.684031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:37.392590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:39.210517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:41.047726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:43.106378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:46.127503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:49.166138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:51.727133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:54.936466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:58.313152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:01.059347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:03.829072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:07.003073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:09.734782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:11.895725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:14.872219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:17.750020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:20.614828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:23.930280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:37.501503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:39.313841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:41.159049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:43.222059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:46.326456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:49.334938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:51.869166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:55.122939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:58.462770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:01.203637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:04.008283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:07.173623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:09.854604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:12.052362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:15.057855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:17.897722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:20.793699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:24.114176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:37.686235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:39.408021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:41.266262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:43.363228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:46.496268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:49.489715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:51.985559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:55.326956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:58.600908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:01.335341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:04.224575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:07.321804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:09.966283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:12.185146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:15.203249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:18.019959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:20.967604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:24.298166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:37.779935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:39.499965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:41.374910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:43.493822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:46.662067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:49.640249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:52.124178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:55.526412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:58.744846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:01.466853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:04.390500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:07.477970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:10.085526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:12.316646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:15.366552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:18.142055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:21.127233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:24.485732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:37.881037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:39.592056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:41.476604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:43.649189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:46.869901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:49.796033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:52.272501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:55.750033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:58.884577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:01.614418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:04.544347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:07.626287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:10.203115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:12.450077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:15.515548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:18.265801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:21.291288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:24.835848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:37.973692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:39.678077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:41.573619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:43.835230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:47.046877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:49.935366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:52.425151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:55.967353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:41:59.019150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:01.772072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:04.724984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:07.779261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:10.322797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:12.578514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:15.657341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:18.436372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T20:42:21.451154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-20T20:42:39.288002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Accepted Credit CardAccepted Home Equity LoanAccepted Personal LoanAccepted RetentionAgeAge Youngest ChildAnnual valueAverage Balance Feed IndexCustomer IDGenderHas Bad Payment RecordHomeownerHousehold Debt to Equity RatioIncomeInterested in Credit CardInterested in Home Equity LoanInterested in Personal LoanInterested in RetentionMarital StatusMembers Within HouseholdMonths Current AccountMonths as a CustomerNumber of Call Center ContactsNumber of Loan AccountsNumber of ProductsNumber of TransactionsNumber of Workers in HouseholdPercentage White Collar WorkersPersonal Debt to Equity RatioRFM ScoreWeeks Since Last Offer
Accepted Credit Card1.0000.0000.0000.0000.0270.0290.0550.0650.0000.0000.0000.0100.0330.0000.0020.0110.0000.0000.0160.0000.0000.0000.0000.0000.0650.0000.0000.0340.0270.0000.064
Accepted Home Equity Loan0.0001.0000.0000.0000.0630.0000.0250.0390.0550.0000.0000.0290.0000.0000.0000.0000.0230.0000.0000.0000.0340.0000.0000.0000.0000.0750.0000.0000.0630.0000.000
Accepted Personal Loan0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0200.0000.0000.0660.0000.0000.0000.0440.0330.0260.0400.0490.0370.0310.0590.0000.0530.0000.0000.0000.000
Accepted Retention0.0000.0000.0001.0000.0200.0280.0260.0000.0000.0250.0000.0000.0000.0000.0000.0000.0000.0170.0350.0330.0420.0380.0000.0490.0000.0000.0760.0000.0200.0510.036
Age0.0270.0630.0000.0201.0000.973-0.007-0.011-0.0170.0300.0000.0000.003-0.0140.0530.0220.0000.0000.3490.177-0.0080.000-0.0310.0090.008-0.006-0.0080.0121.000-0.0010.027
Age Youngest Child0.0290.0000.0000.0280.9731.000-0.003-0.008-0.0220.0320.0000.0370.011-0.0120.0460.0270.0000.0450.2690.180-0.0030.000-0.0300.0090.010-0.002-0.0120.0040.9730.0030.031
Annual value0.0550.0250.0000.026-0.007-0.0031.0000.4670.0390.0000.0000.000-0.0650.0010.0290.0750.0000.0810.1070.0460.0580.0410.0180.8120.5140.4800.0070.065-0.0070.472-0.021
Average Balance Feed Index0.0650.0390.0000.000-0.011-0.0080.4671.0000.0200.0460.0390.000-0.003-0.0470.0260.0000.0670.0150.0190.0450.1860.1200.0120.0370.9100.969-0.0080.007-0.0110.962-0.039
Customer ID0.0000.0550.0000.000-0.017-0.0220.0390.0201.0000.0140.0340.033-0.0030.0180.0000.0000.0380.0000.015-0.0060.0150.0000.0070.0010.0460.0340.036-0.005-0.0170.026-0.005
Gender0.0000.0000.0000.0250.0300.0320.0000.0460.0141.0000.0400.0000.1360.1370.0000.0000.0000.0000.0000.0410.0000.0000.0000.0000.0000.0000.2430.1350.0300.0000.049
Has Bad Payment Record0.0000.0000.0200.0000.0000.0000.0000.0390.0340.0401.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0340.0420.1210.0000.0000.0000.0000.0000.0000.0000.000
Homeowner0.0100.0290.0000.0000.0000.0370.0000.0000.0330.0000.0001.0000.0540.0000.0060.0000.0000.0000.0000.0000.0400.0000.0080.0000.0000.0000.0510.0540.0000.0000.000
Household Debt to Equity Ratio0.0330.0000.0000.0000.0030.011-0.065-0.003-0.0030.1360.0000.0541.0000.0230.0420.0000.0000.0000.1220.017-0.0230.0280.029-0.0980.0080.006-0.364-0.9190.0030.002-0.016
Income0.0000.0000.0660.000-0.014-0.0120.001-0.0470.0180.1370.0000.0000.0231.0000.0000.0000.0000.0000.1390.008-0.0650.049-0.0160.035-0.031-0.0460.029-0.027-0.014-0.045-0.018
Interested in Credit Card0.0020.0000.0000.0000.0530.0460.0290.0260.0000.0000.0000.0060.0420.0001.0000.0340.0000.0140.0470.0000.0520.0340.0000.0000.0330.0850.0000.0560.0530.0640.000
Interested in Home Equity Loan0.0110.0000.0000.0000.0220.0270.0750.0000.0000.0000.0000.0000.0000.0000.0341.0000.0000.0210.0620.0140.0550.0000.0630.0400.0630.0440.0230.0000.0220.0730.000
Interested in Personal Loan0.0000.0230.0000.0000.0000.0000.0000.0670.0380.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0300.0000.0000.0000.0000.0740.0450.0000.0000.0000.0000.000
Interested in Retention0.0000.0000.0440.0170.0000.0450.0810.0150.0000.0000.0000.0000.0000.0000.0140.0210.0001.0000.0000.0000.0000.0000.0000.0500.0170.0000.0340.0370.0000.0400.020
Marital Status0.0160.0000.0330.0350.3490.2690.1070.0190.0150.0000.0000.0000.1220.1390.0470.0620.0000.0001.0000.0640.0000.0000.0000.2050.0000.0110.1250.1590.3490.0190.022
Members Within Household0.0000.0000.0260.0330.1770.1800.0460.045-0.0060.0410.0000.0000.0170.0080.0000.0140.0300.0000.0641.0000.0130.0000.0090.0320.0560.0560.010-0.0130.1770.0560.043
Months Current Account0.0000.0340.0400.042-0.008-0.0030.0580.1860.0150.0000.0340.040-0.023-0.0650.0520.0550.0000.0000.0000.0131.0000.8920.0150.0010.0870.185-0.0100.028-0.0080.182-0.004
Months as a Customer0.0000.0000.0490.0380.0000.0000.0410.1200.0000.0000.0420.0000.0280.0490.0340.0000.0000.0000.0000.0000.8921.0000.0140.0000.0440.0660.0000.0190.0000.1550.000
Number of Call Center Contacts0.0000.0000.0370.000-0.031-0.0300.0180.0120.0070.0000.1210.0080.029-0.0160.0000.0630.0000.0000.0000.0090.0150.0141.0000.0020.0030.017-0.032-0.021-0.0310.020-0.002
Number of Loan Accounts0.0000.0000.0310.0490.0090.0090.8120.0370.0010.0000.0000.000-0.0980.0350.0000.0400.0000.0500.2050.0320.0010.0000.0021.0000.0400.0340.0350.0880.0090.0370.003
Number of Products0.0650.0000.0590.0000.0080.0100.5140.9100.0460.0000.0000.0000.008-0.0310.0330.0630.0740.0170.0000.0560.0870.0440.0030.0401.0000.930-0.016-0.0030.0080.914-0.047
Number of Transactions0.0000.0750.0000.000-0.006-0.0020.4800.9690.0340.0000.0000.0000.006-0.0460.0850.0440.0450.0000.0110.0560.1850.0660.0170.0340.9301.000-0.017-0.000-0.0060.978-0.037
Number of Workers in Household0.0000.0000.0530.076-0.008-0.0120.007-0.0080.0360.2430.0000.051-0.3640.0290.0000.0230.0000.0340.1250.010-0.0100.000-0.0320.035-0.016-0.0171.0000.000-0.008-0.0080.048
Percentage White Collar Workers0.0340.0000.0000.0000.0120.0040.0650.007-0.0050.1350.0000.054-0.919-0.0270.0560.0000.0000.0370.159-0.0130.0280.019-0.0210.088-0.003-0.0000.0001.0000.0120.0010.001
Personal Debt to Equity Ratio0.0270.0630.0000.0201.0000.973-0.007-0.011-0.0170.0300.0000.0000.003-0.0140.0530.0220.0000.0000.3490.177-0.0080.000-0.0310.0090.008-0.006-0.0080.0121.000-0.0010.027
RFM Score0.0000.0000.0000.051-0.0010.0030.4720.9620.0260.0000.0000.0000.002-0.0450.0640.0730.0000.0400.0190.0560.1820.1550.0200.0370.9140.978-0.0080.001-0.0011.000-0.033
Weeks Since Last Offer0.0640.0000.0000.0360.0270.031-0.021-0.039-0.0050.0490.0000.000-0.016-0.0180.0000.0000.0000.0200.0220.043-0.0040.000-0.0020.003-0.047-0.0370.0480.0010.027-0.0331.000

Missing values

2024-11-20T20:42:25.218351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-20T20:42:25.949655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AgeMonths as a CustomerNumber of ProductsRFM ScoreAverage Balance Feed IndexNumber of TransactionsPersonal Debt to Equity RatioMonths Current AccountNumber of Loan AccountsCustomer IDHas Bad Payment RecordMembers Within HouseholdNumber of Call Center ContactsGenderMarital StatusAge Youngest ChildNumber of Workers in HouseholdPercentage White Collar WorkersHousehold Debt to Equity RatioIncomeWeeks Since Last OfferHomeownerAccepted Personal LoanAccepted RetentionAccepted Home Equity LoanAccepted Credit CardAnnual valueInterested in Personal LoanInterested in RetentionInterested in Home Equity LoanInterested in Credit Card
04024.039.8292292401305409059FS116375637073.017FFFFF90FFFF
14712.0211.8088147525477049MM1313305650721.022FFFFF260FFFF
23536.000.00000353105507040FU19335743578.027TFFTT0FFTF
3450.000.0000045-3080111MU157306269553.051FFFFF0FTFF
41348.000.00000133901580611FS07276437502.08TFFFF0FFTF
54512.0211.81911914563173059FS1410286034722.032FTFFF360FTFF
64012.000.0000040505408047MU710404856607.016FFFFF0FFTF
73636.000.00000362805579018FS74365855328.021FFFTF0FFFF
82612.017.8791791268056950510MS015315242752.034FFFFF30FFFT
93336.029.9201201332515732059MS212295771450.046TFFFF160FFTF
AgeMonths as a CustomerNumber of ProductsRFM ScoreAverage Balance Feed IndexNumber of TransactionsPersonal Debt to Equity RatioMonths Current AccountNumber of Loan AccountsCustomer IDHas Bad Payment RecordMembers Within HouseholdNumber of Call Center ContactsGenderMarital StatusAge Youngest ChildNumber of Workers in HouseholdPercentage White Collar WorkersHousehold Debt to Equity RatioIncomeWeeks Since Last OfferHomeownerAccepted Personal LoanAccepted RetentionAccepted Home Equity LoanAccepted Credit CardAnnual valueInterested in Personal LoanInterested in RetentionInterested in Home Equity LoanInterested in Credit Card
19904224.000.0000042145887080111FM134256965755.051FFTFF500FFFF
1991390.000.0000039-21887420611MU813355062888.037FFFTF100FFFF
19923312.038.402691336188859068FU611325554406.018TTFFF190FFFF
1993510.090.0000051-3088973040FS1916166752368.03FFFFF270FTFF
1994380.000.0000038-1076210049FU614444056458.028FFFFT0FFFF
19953848.000.000003835176387058MS86563736646.08FFFFF100FFFF
19961124.027.7377011120076430019FS07335865263.022FTTFT60FFFF
19972336.058.342109223271764850511MS014325363861.06TFTFF250FFFF
19982812.04120.807474132850766780410FS07335865263.012TFFFF1230FTFF
19993024.0112.376109130160767190411FU19335743578.013FFFFF30FTFF